def main()

in scripts/train_instance_seg.py [0:0]

• 92 lines of code
• 17 McCabe index (conditional complexity)

def main(args):
    # Initialize multi-processing
    distributed.init_process_group(backend='nccl', init_method='env://')
    device_id, device = args.local_rank, torch.device(args.local_rank)
    rank, world_size = distributed.get_rank(), distributed.get_world_size()
    torch.cuda.set_device(device_id)

    # Initialize logging
    if rank == 0:
        logging.init(args.log_dir, "training" if not args.eval else "eval")
        summary = tensorboard.SummaryWriter(args.log_dir)
    else:
        summary = None

    # Load configuration
    config = make_config(args)

    # Create dataloaders
    train_dataloader, val_dataloader = make_dataloader(args, config, rank, world_size)

    # Create model
    model = make_model(config, train_dataloader.dataset.num_thing, train_dataloader.dataset.num_stuff)
    if args.resume:
        assert not args.pre_train, "resume and pre_train are mutually exclusive"
        log_debug("Loading snapshot from %s", args.resume)
        snapshot = resume_from_snapshot(model, args.resume, ["body", "rpn_head", "roi_head"])
    elif args.pre_train:
        assert not args.resume, "resume and pre_train are mutually exclusive"
        log_debug("Loading pre-trained model from %s", args.pre_train)
        pre_train_from_snapshots(model, args.pre_train, ["body", "rpn_head", "roi_head"])
    else:
        assert not args.eval, "--resume is needed in eval mode"
        snapshot = None

    # Init GPU stuff
    torch.backends.cudnn.benchmark = config["general"].getboolean("cudnn_benchmark")
    model = DistributedDataParallel(model.cuda(device), device_ids=[device_id], output_device=device_id,
                                    find_unused_parameters=True)

    # Create optimizer
    optimizer, scheduler, batch_update, total_epochs = make_optimizer(config, model, len(train_dataloader))
    if args.resume:
        optimizer.load_state_dict(snapshot["state_dict"]["optimizer"])

    # Training loop
    momentum = 1. - 1. / len(train_dataloader)
    meters = {
        "loss": AverageMeter((), momentum),
        "obj_loss": AverageMeter((), momentum),
        "bbx_loss": AverageMeter((), momentum),
        "roi_cls_loss": AverageMeter((), momentum),
        "roi_bbx_loss": AverageMeter((), momentum),
        "roi_msk_loss": AverageMeter((), momentum)
    }

    if args.resume:
        starting_epoch = snapshot["training_meta"]["epoch"] + 1
        best_score = snapshot["training_meta"]["best_score"]
        global_step = snapshot["training_meta"]["global_step"]
        for name, meter in meters.items():
            meter.load_state_dict(snapshot["state_dict"][name + "_meter"])
        del snapshot
    else:
        starting_epoch = 0
        best_score = 0
        global_step = 0

    # Optional: evaluation only:
    if args.eval:
        log_info("Validating epoch %d", starting_epoch - 1)
        validate(model, val_dataloader, config["optimizer"].getstruct("loss_weights"),
                 device=device, summary=summary, global_step=global_step,
                 epoch=starting_epoch - 1, num_epochs=total_epochs,
                 log_interval=config["general"].getint("log_interval"),
                 coco_gt=config["dataloader"]["coco_gt"], log_dir=args.log_dir)
        exit(0)

    for epoch in range(starting_epoch, total_epochs):
        log_info("Starting epoch %d", epoch + 1)
        if not batch_update:
            scheduler.step(epoch)

        # Run training epoch
        global_step = train(model, optimizer, scheduler, train_dataloader, meters,
                            batch_update=batch_update, epoch=epoch, summary=summary, device=device,
                            log_interval=config["general"].getint("log_interval"), num_epochs=total_epochs,
                            global_step=global_step, loss_weights=config["optimizer"].getstruct("loss_weights"))

        # Save snapshot (only on rank 0)
        if rank == 0:
            snapshot_file = path.join(args.log_dir, "model_last.pth.tar")
            log_debug("Saving snapshot to %s", snapshot_file)
            meters_out_dict = {k + "_meter": v.state_dict() for k, v in meters.items()}
            save_snapshot(snapshot_file, config, epoch, 0, best_score, global_step,
                          body=model.module.body.state_dict(),
                          rpn_head=model.module.rpn_head.state_dict(),
                          roi_head=model.module.roi_head.state_dict(),
                          optimizer=optimizer.state_dict(),
                          **meters_out_dict)

        if (epoch + 1) % config["general"].getint("val_interval") == 0:
            log_info("Validating epoch %d", epoch + 1)
            score = validate(model, val_dataloader, config["optimizer"].getstruct("loss_weights"),
                             device=device, summary=summary, global_step=global_step,
                             epoch=epoch, num_epochs=total_epochs,
                             log_interval=config["general"].getint("log_interval"),
                             coco_gt=config["dataloader"]["coco_gt"], log_dir=args.log_dir)

            # Update the score on the last saved snapshot
            if rank == 0:
                snapshot = torch.load(snapshot_file, map_location="cpu")
                snapshot["training_meta"]["last_score"] = score
                torch.save(snapshot, snapshot_file)
                del snapshot

            if score > best_score:
                best_score = score
                if rank == 0:
                    shutil.copy(snapshot_file, path.join(args.log_dir, "model_best.pth.tar"))